MS damages white matter pathways that connect brain regions, i.e. the structural connectome (SC), disrupting flow of electrical signals, i.e. the functional connectome (FC), causing cognitive and physical disability. Howev- er, the burden of disease in the brain is not always proportional to an individual's disability. How the brain com- pensates for damage in resilient patients remains a mystery, making it difficult to develop accurate prognoses and treatments that can leverage this process in less fortunate patients. Without this knowledge it will not be possible to create individualized therapies based on the brain's natural resiliency mechanism or to establish reliable ways to predict potential for recovery. The thriving field of brain connectivity network analysis, or con- nectomics, provides a promising tool with which to capture, model and understand mechanisms of resiliency. The long-term goal is to develop novel, personalized rehabilitation methods that mimic and enhance the brain's resiliency process to restore cognitive and physical abilities after damage due to neurological conditions. The overall objective of this work is to identify connectome-based imaging biomarkers of resiliency in MS, i.e. those that separate patients with high disease burden and low disability (high-adapters) from those with similar dis- ease burden and high disability (low-adapters). Our central hypothesis, the functional rerouting hypothesis, states that resilient patients' brains recover from injury by restoring normal functional connections using alter- nate white matter pathways to circumvent irrevocably damaged structural connections. This hypothesis is based on published and preliminary work in simulated studies, severe brain injury and mild to moderate trau- matic brain injury. The rationale for the proposed research is that insight into the brain's ability to compensate for injury would allow for more accurate prognostication and enable the development of novel therapeutic strategies for MS. Guided by strong preliminary data, this hypothesis will be tested by pursuing two specific aims: to identify global and regional metrics of the 1) structural and functional connectomes and 2) structure- function relationship between the connectomes that differentiate high-adapting and low-adapting MS patients. We will collect functional, diffusion and anatomical MRIs from 25 controls and 42 high- and 42 low-adapting MS patients to extract structural and functional connectomes and test central hypotheses. The approach is in- novative, in the applicant's opinion, as it implements cutting-edge machine learning techniques and a novel mathematical model to formalize the relationship between structural and functional connectomes and capture network-level functional rerouting in resiliency to MS-related damage. The proposed research is significant in that it is expected to have broad translational impact on the development of more accurate prognoses as well as targeted, personalized treatments for patients with MS and other neurological disorders. Ultimately, such knowledge has the potential to open up new horizons for innovative therapies, e.g. through non-invasive brain stimulation, that can dramatically improve the quality of life for patients with debilitating neurological disease.